The present invention relates generally to motors and, more particularly, to a system and method for detection of incipient conditions indicative of motor faults.
Three-phase induction motors consume a large percentage of generated electricity capacity. Many applications for this “workhorse” of industry are fan and pump industrial applications. For example, in a typical integrated paper mill, low voltage and medium voltage motors may comprise nearly 70% of all driven electrical loads. Due to the prevalence of these motors in industry, it is paramount that the three-phase motor be reliable. Industry reliability surveys suggest that motor failures typically fall into one of four major categories. Specifically, motor faults typically result from bearing failure, stator turn faults, rotor bar failure, or other general faults/failures. Within these four categories: bearing, stator, and rotor failure account for approximately 85% of all motor failures.
It is believed that this percentage could be significantly reduced if the driven equipment were better aligned when installed, and remained aligned regardless of changes in operating conditions. However, motors are often coupled to misaligned pump loads or loads with rotational unbalance and fail prematurely due to stresses imparted upon the motor bearings. Furthermore, manually detecting such fault causing conditions is difficult at best because doing so requires the motor to be running. As such, an operator is usually required to remove the motor from operation to perform a maintenance review and diagnosis. However, removing the motor from service is undesirable in many applications because motor down-time can be extremely costly.
As such, some detection devices have been designed that generate feedback regarding an operating motor. The feedback is then reviewed by an operator to determine the operating conditions of the motor. However, most systems that monitor operating motors merely provide feedback of faults that have likely already damaged the motor. As such, though operational feedback is sent to the operator, it is usually too late for preventive action to be taken.
Some systems have attempted to provide an operator with early fault warning feedback. For example, vibration monitoring has been utilized to provide some early misalignment or unbalance-based faults. However, when a mechanical resonance occurs, machine vibrations are amplified. Due to this amplification, false positives indicating severe mechanical asymmetry are possible. Furthermore, vibration-based monitoring systems typically require highly invasive and specialized monitoring systems to be deployed within the motor system.
In light of the drawbacks of vibration-based monitoring, current-based monitoring techniques have been developed to provide a more inexpensive, non-intrusive technique for detecting bearing faults. There are also limitations and drawbacks to present current-based fault detection. That is, in current-based bearing fault detection, it can be challenging to extract a fault signature from the motor stator current. For different types of bearing faults, fault signatures can be in different forms. According to general fault development processes, bearing faults can be categorized as single-point defects or generalized roughness. Most current-based bearing fault detection techniques currently in use today are directed toward detecting single-point defects and rely on locating and processing the characteristic bearing fault frequencies in the stator current. Such techniques, however, may not be suitable for detecting generalized roughness faults. That is, generalized-roughness faults exhibit degraded bearing surfaces, but not necessarily distinguished defects and, therefore, characteristic fault frequencies may not necessarily exist in the stator current. As many bearing faults initially develop as generalized-roughness bearing faults, especially at an early stage, it would be beneficial for current-based bearing fault detection techniques to be able to detect such generalized-roughness bearing faults.
It would therefore be desirable to design a current-based bearing fault detection technique that overcomes the aforementioned drawbacks. A current-based bearing fault detection technique that allows for detection of generalized-roughness bearing faults would be beneficial, by providing early stage detection of bearing faults.